From: Jason Ni <redacted>
Date: Thu, 14 Aug 2025 11:17:51 +0000 (+0800)
Subject: ggml: fix ggml_conv_1d_dw bug (#1323)
X-Git-Tag: upstream/0.0.2446^0
X-Git-Url: https://git.djapps.eu/?a=commitdiff_plain;h=b141fc226b68e4af383101c39da90b54ede98850;p=pkg%2Fggml%2Fsources%2Fggml

ggml: fix ggml_conv_1d_dw bug (#1323)

* ggml: fix ggml_conv_1d_dw bug

* Fixed conv1d_dw weight tensor dimension.
---

diff --git a/src/ggml.c b/src/ggml.c
index 54961213..a4417f1a 100644
--- a/src/ggml.c
+++ b/src/ggml.c
@@ -4272,14 +4272,13 @@ struct ggml_tensor * ggml_conv_1d_dw(
         int                   s0,
         int                   p0,
         int                   d0) {
-    struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], 1, a->ne[1], a->ne[2]);
     struct ggml_tensor * new_b = ggml_reshape_4d(ctx, b, b->ne[0], 1, b->ne[1], b->ne[2]);
 
-    struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16);
+    struct ggml_tensor * im2col = ggml_im2col(ctx, a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16);
 
     struct ggml_tensor * result = ggml_mul_mat(ctx, im2col, a);
 
-    result = ggml_reshape_3d(ctx, result, b->ne[0], b->ne[1], 1);
+    result = ggml_reshape_3d(ctx, result, result->ne[0], result->ne[2], 1);
 
     return result;
 }
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 701807fc..7c28e344 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -300,6 +300,24 @@ if (NOT GGML_BACKEND_DL)
     add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}>)
     set_property(TEST ${TEST_TARGET} PROPERTY ENVIRONMENT "LLVM_PROFILE_FILE=${TEST_TARGET}.profraw")
 
+    #
+    # test-conv1d-dw-c1
+
+    set(TEST_TARGET test-conv1d-dw-c1)
+    add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
+    target_link_libraries(${TEST_TARGET} PRIVATE ggml)
+    add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}>)
+    set_property(TEST ${TEST_TARGET} PROPERTY ENVIRONMENT "LLVM_PROFILE_FILE=${TEST_TARGET}.profraw")
+
+    #
+    # test-conv1d-dw-c2
+
+    set(TEST_TARGET test-conv1d-dw-c2)
+    add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
+    target_link_libraries(${TEST_TARGET} PRIVATE ggml)
+    add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}>)
+    set_property(TEST ${TEST_TARGET} PROPERTY ENVIRONMENT "LLVM_PROFILE_FILE=${TEST_TARGET}.profraw")
+
     #
     # test-conv2d
 
diff --git a/tests/test-conv1d-dw-c1.cpp b/tests/test-conv1d-dw-c1.cpp
new file mode 100644
index 00000000..fd2d2437
--- /dev/null
+++ b/tests/test-conv1d-dw-c1.cpp
@@ -0,0 +1,243 @@
+#include "ggml.h"
+#include "ggml-cpu.h"
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+
+#ifdef GGML_USE_METAL
+#include "ggml-metal.h"
+#endif
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <vector>
+
+static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    fputs(text, stderr);
+    fflush(stderr);
+}
+
+struct test_model {
+    struct ggml_tensor * weight;
+    struct ggml_tensor * input;
+    ggml_backend_t backend = NULL;
+    ggml_backend_buffer_t buffer;
+    struct ggml_context * ctx;
+};
+
+void load_model(test_model & model, bool use_gpu = false) {
+    // create data
+    int K = 3, IC = 2, OC = 2;
+    int IL = 6, N = 1;
+
+    // Initialize adata
+    float weight_data[6] = {10.0f, 20.0f, 30.0f, 0.1f, 0.2f, 0.3f};
+
+    // Convert adata to fp16 format
+    std::vector<ggml_fp16_t> h_weight_data(K * IC);
+    ggml_fp32_to_fp16_row(weight_data, h_weight_data.data(), K * IC);
+
+    // Initialize input data, 2 channels, 6 timesteps, 1 batch
+    float input_data[12] = {
+        1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+        1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+    };
+
+    size_t buffer_size = 0;
+    {
+        buffer_size += K * IC * ggml_type_size(GGML_TYPE_F16); // tensor weight
+        buffer_size += IL * IC * N * ggml_type_size(GGML_TYPE_F32); // tensor input
+        buffer_size += 1024; // overhead
+    }
+
+    printf("%s: ggml tensor size    = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
+    printf("%s: backend buffer size = %0.2f MB\n", __func__, (buffer_size/ 1024.f/ 1024.f));
+
+    ggml_log_set(ggml_log_callback_default, nullptr);
+
+    int num_tensors = 2;
+    struct ggml_init_params params {
+            /*.mem_size   =*/ ggml_tensor_overhead() * num_tensors,
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+    };
+
+    // initialize the backend
+#ifdef GGML_USE_CUDA
+    if (use_gpu) {
+        fprintf(stderr, "%s: using CUDA backend\n", __func__);
+        model.backend = ggml_backend_cuda_init(0);
+        if (!model.backend) {
+            fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
+        }
+    }
+#endif
+
+#ifdef GGML_USE_METAL
+    if (use_gpu) {
+        fprintf(stderr, "%s: using Metal backend\n", __func__);
+        model.backend = ggml_backend_metal_init();
+        if (!model.backend) {
+            fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
+        }
+    }
+#endif
+
+    if(!model.backend) {
+        // fallback to CPU backend
+        model.backend = ggml_backend_cpu_init();
+    }
+
+    model.buffer = ggml_backend_alloc_buffer(model.backend, buffer_size);
+
+    // create context
+    model.ctx = ggml_init(params);
+
+    // create tensors
+    // A Pytorch grouped Conv1d weight parameter is of shape (out_channels, input_channels/groups, kernel_size)
+    model.weight = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F16,  K, 1, IC);
+    model.input = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, IL, IC, N);
+
+    // create a allocator
+    ggml_tallocr alloc = ggml_tallocr_new(model.buffer);
+
+    // alloc memory
+    ggml_tallocr_alloc(&alloc, model.weight);
+
+    // load data to buffer
+    if(ggml_backend_is_cpu(model.backend)) {
+        memcpy(model.weight->data, h_weight_data.data(), ggml_nbytes(model.weight));
+    } else {
+        ggml_backend_tensor_set(model.weight, h_weight_data.data(), 0, ggml_nbytes(model.weight));
+    }
+
+    // alloc memory
+    ggml_tallocr_alloc(&alloc, model.input);
+
+    if(ggml_backend_is_cpu(model.backend)
+#ifdef GGML_USE_METAL
+                || ggml_backend_is_metal(model.backend)
+#endif
+    ) {
+        memcpy(model.input->data, input_data, ggml_nbytes(model.input));
+    } else {
+        ggml_backend_tensor_set(model.input, input_data, 0, ggml_nbytes(model.input));
+    }
+}
+
+struct ggml_cgraph * build_graph(const test_model& model) {
+    static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
+    static std::vector<uint8_t> buf(buf_size);
+
+    struct ggml_init_params params0 = {
+        /*.mem_size   =*/ buf_size,
+        /*.mem_buffer =*/ buf.data(),
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
+    };
+
+    // create a temporally context to build the graph
+    struct ggml_context * ctx0 = ggml_init(params0);
+
+    struct ggml_cgraph  * gf = ggml_new_graph(ctx0);
+
+    int s0 = 1;
+    int p0 = 1;
+    int d0 = 1;
+
+    struct ggml_tensor* conv1d_dw_res = ggml_conv_1d_dw(ctx0, model.weight, model.input, s0, p0, d0);
+    ggml_set_name(conv1d_dw_res, "conv1d_dw_res");
+    ggml_build_forward_expand(gf, conv1d_dw_res);
+
+    // delete the temporally context used to build the graph
+    ggml_free(ctx0);
+    return gf;
+}
+
+struct ggml_cgraph* compute_graph(const test_model & model, ggml_gallocr_t allocr) {
+    struct ggml_cgraph * gf = build_graph(model);
+
+    // allocate tensors
+    ggml_gallocr_alloc_graph(allocr, gf);
+    int n_threads = 1;
+
+    if (ggml_backend_is_cpu(model.backend)) {
+        ggml_backend_cpu_set_n_threads(model.backend, n_threads);
+    }
+
+    ggml_backend_graph_compute(model.backend, gf);
+
+    //ggml_graph_print(gf);
+
+    return gf;
+}
+
+int main(void)
+{
+    ggml_time_init();
+
+    test_model model;
+    load_model(model, true);
+
+    ggml_gallocr_t allocr = NULL;
+
+    {
+        allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
+
+        //create the worst case graph for memory usage estimation
+        struct ggml_cgraph * gf = build_graph(model);
+
+        // compute the required memory
+        ggml_gallocr_reserve(allocr, gf);
+        size_t mem_size = ggml_gallocr_get_buffer_size(allocr, 0);
+        fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0f/1024.0f);
+    }
+
+    struct ggml_cgraph * gf_res = compute_graph(model, allocr);
+
+    struct ggml_tensor * conv1d_dw_res = NULL;
+
+    for(int i = 0; i < ggml_graph_n_nodes(gf_res); i++) {
+        if(strcmp(ggml_get_name(ggml_graph_node(gf_res, i)), "conv1d_dw_res") == 0) {
+            conv1d_dw_res = ggml_graph_node(gf_res, i);
+        }
+    }
+
+    std::vector<float> conv2d_data(ggml_nelements(conv1d_dw_res));
+
+    ggml_backend_tensor_get(conv1d_dw_res, conv2d_data.data(), 0, ggml_nbytes(conv1d_dw_res));
+
+    const int n_conv1d_dw_test = 12;
+
+    float expected_conv1d_dw[n_conv1d_dw_test] = {
+        50.0f, 60.0f, 60.0f, 60.0f, 60.0f, 30.0f, 0.50f,  0.60f,  0.60f,  0.60f,  0.60f,  0.30f
+    };
+
+    printf("\nPerforming test:\n");
+
+    bool passed = true;
+    passed = true;
+    for(int i = 0; i < n_conv1d_dw_test; i++) {
+        if(std::abs(conv2d_data[i] - expected_conv1d_dw[i]) > 1e-4) {
+            passed = false;
+            break;
+        }
+    }
+
+    printf("ggml_conv1d (%d): %s\n", (int) ggml_nelements(conv1d_dw_res), passed && (ggml_nelements(conv1d_dw_res) == n_conv1d_dw_test) ? "\033[32mPASSED\033[0m" : "\033[31mFAILED\033[0m");
+    ggml_free(model.ctx);
+
+    ggml_backend_buffer_free(model.buffer);
+    ggml_backend_free(model.backend);
+    ggml_gallocr_free(allocr);
+    return 0;
+}
diff --git a/tests/test-conv1d-dw-c2.cpp b/tests/test-conv1d-dw-c2.cpp
new file mode 100644
index 00000000..c7b9a0a7
--- /dev/null
+++ b/tests/test-conv1d-dw-c2.cpp
@@ -0,0 +1,243 @@
+#include "ggml.h"
+#include "ggml-cpu.h"
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+
+#ifdef GGML_USE_METAL
+#include "ggml-metal.h"
+#endif
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <vector>
+
+static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    fputs(text, stderr);
+    fflush(stderr);
+}
+
+struct test_model {
+    struct ggml_tensor * weight;
+    struct ggml_tensor * input;
+    ggml_backend_t backend = NULL;
+    ggml_backend_buffer_t buffer;
+    struct ggml_context * ctx;
+};
+
+void load_model(test_model & model, bool use_gpu = false) {
+    // create data
+    int K = 3, IC = 2, OC = 2;
+    int IL = 6, N = 1;
+
+    // Initialize adata
+    float weight_data[6] = {10.0f, 20.0f, 30.0f, 0.1f, 0.2f, 0.3f};
+
+    // Convert adata to fp16 format
+    std::vector<ggml_fp16_t> h_weight_data(K * IC);
+    ggml_fp32_to_fp16_row(weight_data, h_weight_data.data(), K * IC);
+
+    // Initialize input data, 2 channels, 6 timesteps, 1 batch
+    float input_data[12] = {
+        1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+        1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+    };
+
+    size_t buffer_size = 0;
+    {
+        buffer_size += K * IC * ggml_type_size(GGML_TYPE_F16); // tensor weight
+        buffer_size += IL * IC * N * ggml_type_size(GGML_TYPE_F32); // tensor input
+        buffer_size += 1024; // overhead
+    }
+
+    printf("%s: ggml tensor size    = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
+    printf("%s: backend buffer size = %0.2f MB\n", __func__, (buffer_size/ 1024.f/ 1024.f));
+
+    ggml_log_set(ggml_log_callback_default, nullptr);
+
+    int num_tensors = 2;
+    struct ggml_init_params params {
+            /*.mem_size   =*/ ggml_tensor_overhead() * num_tensors,
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+    };
+
+    // initialize the backend
+#ifdef GGML_USE_CUDA
+    if (use_gpu) {
+        fprintf(stderr, "%s: using CUDA backend\n", __func__);
+        model.backend = ggml_backend_cuda_init(0);
+        if (!model.backend) {
+            fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
+        }
+    }
+#endif
+
+#ifdef GGML_USE_METAL
+    if (use_gpu) {
+        fprintf(stderr, "%s: using Metal backend\n", __func__);
+        model.backend = ggml_backend_metal_init();
+        if (!model.backend) {
+            fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
+        }
+    }
+#endif
+
+    if(!model.backend) {
+        // fallback to CPU backend
+        model.backend = ggml_backend_cpu_init();
+    }
+
+    model.buffer = ggml_backend_alloc_buffer(model.backend, buffer_size);
+
+    // create context
+    model.ctx = ggml_init(params);
+
+    // create tensors
+    // A Pytorch grouped Conv1d weight parameter is of shape (out_channels, input_channels/groups, kernel_size)
+    model.weight = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F16,  K, 1, IC);
+    model.input = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, IL, IC, N);
+
+    // create a allocator
+    ggml_tallocr alloc = ggml_tallocr_new(model.buffer);
+
+    // alloc memory
+    ggml_tallocr_alloc(&alloc, model.weight);
+
+    // load data to buffer
+    if(ggml_backend_is_cpu(model.backend)) {
+        memcpy(model.weight->data, h_weight_data.data(), ggml_nbytes(model.weight));
+    } else {
+        ggml_backend_tensor_set(model.weight, h_weight_data.data(), 0, ggml_nbytes(model.weight));
+    }
+
+    // alloc memory
+    ggml_tallocr_alloc(&alloc, model.input);
+
+    if(ggml_backend_is_cpu(model.backend)
+#ifdef GGML_USE_METAL
+                || ggml_backend_is_metal(model.backend)
+#endif
+    ) {
+        memcpy(model.input->data, input_data, ggml_nbytes(model.input));
+    } else {
+        ggml_backend_tensor_set(model.input, input_data, 0, ggml_nbytes(model.input));
+    }
+}
+
+struct ggml_cgraph * build_graph(const test_model& model) {
+    static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
+    static std::vector<uint8_t> buf(buf_size);
+
+    struct ggml_init_params params0 = {
+        /*.mem_size   =*/ buf_size,
+        /*.mem_buffer =*/ buf.data(),
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
+    };
+
+    // create a temporally context to build the graph
+    struct ggml_context * ctx0 = ggml_init(params0);
+
+    struct ggml_cgraph  * gf = ggml_new_graph(ctx0);
+
+    int s0 = 3;
+    int p0 = 0;
+    int d0 = 1;
+
+    struct ggml_tensor* conv1d_dw_res = ggml_conv_1d_dw(ctx0, model.weight, model.input, s0, p0, d0);
+    ggml_set_name(conv1d_dw_res, "conv1d_dw_res");
+    ggml_build_forward_expand(gf, conv1d_dw_res);
+
+    // delete the temporally context used to build the graph
+    ggml_free(ctx0);
+    return gf;
+}
+
+struct ggml_cgraph* compute_graph(const test_model & model, ggml_gallocr_t allocr) {
+    struct ggml_cgraph * gf = build_graph(model);
+
+    // allocate tensors
+    ggml_gallocr_alloc_graph(allocr, gf);
+    int n_threads = 1;
+
+    if (ggml_backend_is_cpu(model.backend)) {
+        ggml_backend_cpu_set_n_threads(model.backend, n_threads);
+    }
+
+    ggml_backend_graph_compute(model.backend, gf);
+
+    //ggml_graph_print(gf);
+
+    return gf;
+}
+
+int main(void)
+{
+    ggml_time_init();
+
+    test_model model;
+    load_model(model, true);
+
+    ggml_gallocr_t allocr = NULL;
+
+    {
+        allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
+
+        //create the worst case graph for memory usage estimation
+        struct ggml_cgraph * gf = build_graph(model);
+
+        // compute the required memory
+        ggml_gallocr_reserve(allocr, gf);
+        size_t mem_size = ggml_gallocr_get_buffer_size(allocr, 0);
+        fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0f/1024.0f);
+    }
+
+    struct ggml_cgraph * gf_res = compute_graph(model, allocr);
+
+    struct ggml_tensor * conv1d_dw_res = NULL;
+
+    for(int i = 0; i < ggml_graph_n_nodes(gf_res); i++) {
+        if(strcmp(ggml_get_name(ggml_graph_node(gf_res, i)), "conv1d_dw_res") == 0) {
+            conv1d_dw_res = ggml_graph_node(gf_res, i);
+        }
+    }
+
+    std::vector<float> conv2d_data(ggml_nelements(conv1d_dw_res));
+
+    ggml_backend_tensor_get(conv1d_dw_res, conv2d_data.data(), 0, ggml_nbytes(conv1d_dw_res));
+
+    const int n_conv1d_dw_test = 4;
+
+    float expected_conv1d_dw[n_conv1d_dw_test] = {
+        60.0f, 60.0f, 0.6f, 0.6f
+    };
+
+    printf("\nPerforming test:\n");
+
+    bool passed = true;
+    passed = true;
+    for(int i = 0; i < n_conv1d_dw_test; i++) {
+        if(std::abs(conv2d_data[i] - expected_conv1d_dw[i]) > 1e-4) {
+            passed = false;
+            break;
+        }
+    }
+
+    printf("ggml_conv1d (%d): %s\n", (int) ggml_nelements(conv1d_dw_res), passed && (ggml_nelements(conv1d_dw_res) == n_conv1d_dw_test) ? "\033[32mPASSED\033[0m" : "\033[31mFAILED\033[0m");
+    ggml_free(model.ctx);
+
+    ggml_backend_buffer_free(model.buffer);
+    ggml_backend_free(model.backend);
+    ggml_gallocr_free(allocr);
+    return 0;
+}